s2fieldsofstudy: Biology Chemistry

text:
Prominent Effects of Berbamine Hydrochloride on Alzheimer’s Disease Model Mice

Very few anti-Alzheimer’s disease (AD) drugs are clinically available at present due to the complex mechanism of Alzheimer’s disease. For the purpose of discovering potential anti-AD drugs in bisbenzylisoquinoline alkaloids, the anti-AD function and the mechanism of the function of berbamine hydrochloride (BBMH) were studied. Three kinds of AD model mice, double transgenic APP/PS1 AD mice, Gal-Alu AD mice induced by the intraperitoneal injection of d-galactose combined with the intragastric administration of aluminum trichloride, and Alu AD-like mice induced by stereotactic brain injection of aluminum trichloride, were administered with BBMH for 40 days at a dosage of 280 mg/kg/d. The effects of BBMH on the learning and memory behavior of the AD mice were studied through the Morris water maze experiment, and the influences of BBMH on the pathological features of AD, including the deposition of Aβ, the lesions of pyramidal cells (neurons), and the formation of neurofibrillary tangles, were studied by the immunohistochemical staining, hematoxylin-eosin staining, and silver staining of the brain tissues of the mice. The water maze experiment showed that BBMH could significantly improve the learning and memory abilities of three kinds of treated mice. Immunohistochemical staining showed that BBMH could significantly reduce the deposition of Aβ in the brain tissues of treated mice. Hematoxylin-eosin staining showed that BBMH could significantly alleviate the lesions of pyramidal cells in the hippocampal tissue of the mice. Silver staining showed that BBMH could significantly reduce the formation of neurofibrillary tangles in the hippocampal tissue of the mice. These results indicated that BBMH has significant anti-AD effects and the potential as an anti-AD drug. Western blot analysis of the brain tissue of the mice showed that the expression level of calpain, a Ca2+-dependent proteolytic enzyme, was significantly inhibited and the expression level of SelK, a selenoprotein mainly expressed in immune cells, was significantly increased. It is speculated that the anti-AD effect of BBMH is related to the improvement of the phagocytosis of microglial cells in brain tissues and macrophages migrated into the brain as well as the regulation of calcium homeostasis and calcium-dependent proteases in the brain tissues of the mice.

INTRODUCTION
Alzheimer's disease (AD) is a disease with high incidence in the elderly, which seriously endangers the life quality of people (Lane et al., 2018). Unfortunately, due to its complex etiology, currently there are limited anti-AD drugs that can be used clinically (Ju et al., 2021). Many hypotheses about the pathogenesis of the disease have been proposed by researchers, including the amyloid cascade hypothesis (Hardy and Higgins, 2019), presenilin hypothesis (Shen and Kelleher, 2007), APP matrix hypothesis (Hunter and Brayne, 2018), tau hyperphosphorylation hypothesis (Iqbal et al., 2016), neuroinflammation hypothesis (Heppner et al., 2015), oxidative stress hypothesis (Cheignon et al., 2018), mitochondrial dysfunction hypothesis (Pei and Wallace, 2018), ER stress hypothesis (Hetz and Saxena, 2017), glucose regulation disorder hypothesis (Neth and Craft, 2017), and cholesterol and metal metabolism hypothesis (Wong et al., 2014).
The discovery of potential drugs derived from natural products is an important way to develop new drugs (Chen C. et al., 2021;Wang et al., 2021). Bisbenzylisoquinoline alkaloids such as liensinine, isoliensinine, puerarin, tetrandrine, and berbamine have neuroprotective and anti-inflammatory effects mainly as calcium antagonists (Sharma et al., 2017;Chan et al., 2021;Kim et al., 2021;Guo et al., 2022). Previous studies indicated that liensinine and isoliensinine exhibited certain anti-AD effects (Meng et al., 2019). Berbamine (BBM), one of the bio-active alkaloids, is often present in the form of berbamine hydrochloride (BBMH). BBMH has been clinically used to promote leukocyte hyperplasia, anti-tumor, and protect the immune system and cardiovascular system (Xiping et al., 2016;Jia et al., 2017;Zhang et al., 2012).
For the purpose of studying the neuroprotective effects of BBMH on AD, three kinds of AD model mice including double transgenic APP/PS1 AD mice, Gal-Alu AD mice induced by the intraperitoneal injection of D-galactose combined with the intragastric administration of aluminum trichloride, and Alu AD mice induced by stereotactic brain injection of aluminum trichloride were prepared first. Then, they were administered with BBMH for 40 days at a dosage of 280 mg/kg/d by gavage according to the previously reported (Wang et al., 2006) and the results of the preexperiment. Subsequently, the effect of BBMH on the learning and memory ability of AD mice was investigated through the Morris water maze experiment, and the influence of BBMH on the state of pyramidal cells and the formation of Aβ deposition was studied by hematoxylin-eosin staining and immunohistochemical staining of mouse brain hippocampal tissue. The influence of BBMH on the formation of neurofibrillary tangles in mouse hippocampal tissue was investigated by glycine silver staining. Finally, the effect of BBMH on the expression of Ca 2+ -dependent proteolytic enzyme, calpain, and its target protein, selenoprotein SelK, was tested by the method of Western blot.

Drugs and Reagents
BBMH was purchased from Chengdu Delico Biotechnology Co., LTD. Streptavidin-HRP kit (DAB), SP rabbit HRP kit (DAB), citrate buffer (100×), and BCA protein assay kit were purchased from Beijing Kang Wei Century Biotechnology Co., LTD. Sodium pentobarbital, edible melanin, and physiological saline were purchased from Shenyang Laibo Science and Trade Co., LTD. D-Galactose was purchased from Beijing Dingguo Changsheng Biotechnology Co., LTD. Anhydrous ethanol, n-butanol, and xylene were purchased from Kelon Chemical Reagent Factory in Chengdu. Hematoxylin dye and neutral resin were purchased from Beijing Dingguo Changsheng Biotechnology Co., LTD. Anti-fluorescence quenching sealing tablets and aluminum chloride were purchased from Shanghai Maclin Biochemical Technology Co., LTD. Bovine serum albumin (BSA) was purchased from Beijing Solebo Technology Co., LTD. Silver glycine dye set, tissue fixative fluid, and HE dye were purchased from Wuhan Xavier Biotechnology Co., LTD. First antibody of rabbit anti-beta-amyloid 1-40 (CT) antibody and rabbit anti-β-actin were purchased from Beijing Biosynthesis Biotechnology Co., LTD. First antibody of rabbit calpain two polyclonal antibody and rabbit SELK polyclonal antibody were purchased from Proteintech Inc. Secondary antibodies of HRPconjugated goat anti-rabbit antibody was purchased from Beyotime Co., LTD. The ECL reagents were purchased from Tanon (Shanghai, China).

Preparation of APP/PS1 Transgenic Mice and BBMH Administration of the Mice
The parental APP/PS1 mice were donated by PLA Northern Theater Command Hospital. The APP/PS1 offspring mice were generated by the crossing of parental APP/PS1 mice. The double transgenic progeny of APP/PS1 mice were identified by a mouse tail gene identification kit (Lin et al., 2018). Twelve six-month-old APP/PS1 male mice were randomly divided into two groups, the administration group and the model group. The mice in the administration group were given 280 mg/kg/d (suspended in physiological saline solution at 1:4 w/w) BBMH for 40 days by gavage, and the model AD group and normal control mice (6month-old wild-type mice) were given the same amount of physiological saline solution for 40 days.

Preparation of Gal-Alu AD Model Mice and BBMH Administration of the Mice
The male mice were purchased from Liaoning Changsheng Biotechnology Co., LTD (Kunming male SPF mice, Animal License: SCXK Liao, 2010-0001) at the age of 6-7 weeks (weight 20-22 g). For the purpose of preparing AD model mice, they were given 20 mg/kg/d aluminum chloride solution by gavage and 120 mg/kg/d D-galactose solution (dissolved in normal saline solution) intraperitoneally injected continuously for 40 days according to the method (Xing et al., 2018) with minor modification. Then, 16 modeled AD mice were randomly divided into two groups including the BBMH administration group and the Gal-Alu model AD group. The former was given BBMH solution (dissolved in normal saline) at the dosage of 280 mg/kg/d by gavage for 40 days, and the latter was given the same amount of normal saline for 40 days, respectively.

Preparation of Alu AD Mice and BBMH Administration of the Mice
After being anesthetized by the intraperitoneal injection of 4% sodium pentobarbital at a dosage of 0.025 ml/10 g, the male mice were fixed on the brain stereotaxic instrument and 3 μl of 2.5% aluminum trichloride was injected into the brain hippocampal tissues of the mice using a microsyringe (Zhang and Yu, 2002). The sham operation group was injected with an equal volume of saline. After being fed for another 2 months, they were randomly divided into two groups including the BBMH administration group and the Alu model AD group. The former was given BBMH at the dosage of 280 mg/kg/d by gavage for 40 days, while the latter was given the same amount of normal saline for 40 days, respectively.

Measurement of the Learning and Memory Abilities of Mice by the Morris Water Maze Test
The Morris water maze test was conducted as described in the previous work (Tang et al., 2020) with minor modifications. There are I, II, III, and VI quadrants in the Morris water maze (Shanghai XinRuan Information Technology Co., Ltd., Shanghai, China) for mice. The platform was fixed in the center of the third quadrant (target quadrant) and submerged approximately 1 cm beneath the water surface. The training trial was repeated for four consecutive days. For each trial, the mice were gently released from the first to fourth quadrants; sequentially, the mice were put gently into the water (temperature: 22 ± 1°C) facing the wall of the water tank and avoiding making noise and other disturbances to the mice. The trial was terminated if the mouse failed to climb onto the platform within 90 s. Also, the time to reach the platform (escape latency) and path length of the mice were automatically recorded using a video recording system. If the mouse did not find the platform within 90 s, it was guided to the platform and stayed there for 20 s, and the escape latency was recorded as 90 s.
The probe trial was administered 24 h after the last training session. In the probe trial, the platform was removed from the pool, and the mice were gently placed in the opposite quadrant of the third quadrant (the target quadrant), facing the wall of the water tank. Then, the time of mice spent swimming in the third quadrant housing the platform (target quadrant) within 90 s and the number of crossing the previous platform location in the target quadrant were measured using an automated analysis system.

Preparation of the Paraffin Section
After being anesthetized by the intraperitoneal injection of 4% sodium pentobarbital, the mice were then killed by removal of the neck, and the whole brain tissues of five mice for every treatment were taken out and put into 100 ml tissue fixation solution for more than 24 h. The fixed brain tissues of mice were soaked in 70, 85, 95, and 100% ethanol for several hours successively for dehydration and then soaked in a mixture of xylene and ethanol (V/V, 50/50) for 10 min and twice in xylene for 10 min, and finally three times (45, 60, and 60 min, respectively) in paraffin at 65°C for waxing. After dehydration and wax immersion, the brain tissues of the mice were quickly put into an embedded frame containing 10 ml wax solution at 65°C. The wax solution was cooled to −20°C, and the brain tissues of the mice were solidified. The brain tissues in wax were cut into slices of 4 μm thickness using a paraffin-sectioning machine.

Immunohistochemical Staining
The slice on the slide was soaked in xylene I, xylene II, xylene III, and anhydrous ethanol for 10 min successively, then soaked in 95% ethanol and 90% ethanol three times, and finally washed with PBS solution three times (3 min for each time). Then, the slide was placed in citric acid solution and maintained at 105°C for 10 min in a pressure cooker for antigen repairing. At last, the slide was placed in PBS solution (pH = 7.4) and washed three times (5 min for each time) on an automatic shaker. Endogenous peroxidase blocker (3% H 2 O 2 deionized water) was carefully added to the tissue on the slide for 10 min, and the slide was then placed in PBS solution (pH = 7.4) and washed with PBS three times again.
After the tissue on the slide was sealed with the sealing solution for 15 min at room temperature, the first antibody of rabbit antibeta-amyloid 1-40 (CT) antibody (dilution of 1:500) was added on the tissue on the slide and placed in a dark box overnight at 4°C. Then, a secondary antibody corresponding to the first antibody, biotinylated antibody labeled with HRP, was dropped onto the tissue and incubated for 30 min at room temperature. Then, the slides were placed in PBS solution and washed on a shaker three times (5 min for each time). After the slides were slightly dried, the newly prepared DAB staining solution was carefully added to cover the whole tissue. When the tan-positive reaction was observed under a microscope, the slides were rinsed with running water to stop the staining.
After the slide was dehydrated with 90% ethanol, 95% ethanol, 100% ethanol, n-butanol, xylene I, and xylene II, it was sealed with neutral gum and observed using an inverted fluorescence microscope.

Hematoxylin-Eosin Staining
After the dewaxing and hydrating of the slide similar to immunohistochemical staining, the slide was put into hematoxylin-dyeing solution and stained for 3-5 min. The clean slide was soaked in 85% ethanol for 5 min successively, and then it was put into eosin staining solution for another 5 min and washed with double steaming water. Then, the slide was dehydrated and sealed and observed using an inverted microscope.

Silver Staining for Neurofibrillary Tangles
After the dewaxing and hydrating of the slide similar to immunohistochemical staining, the slide was put into silver glycine dye C to stain for 5 min, and then it was rinsed with double steaming water three times. Then, the slide was soaked in silver glycine solution B for 3-5 min. At last, the slide was put into preheated silver glycine dye solution A I (45°C) and preheated silver glycine dye solution A II (45°C) for another few seconds; then, the slide was washed with distilled water. Finally, the slide was dehydrated and sealed and observed using an inverted microscope.

Western Blot
The Western blot analysis was achieved as the reported method with minor modifications (Meng et al., 2017). Briefly, the mice were sacrificed by neck removal; the hippocampus and cortex tissues were taken out and washed with cold PBS 3 times and put into a glass homogenizer. After 0.25-0.3 ml of tissue lysate was added, the tissues of about 20 mg were thoroughly ground to form a homogenate. The lysed tissue homogenate was put into the centrifuge and centrifuged at 12000 RPM and 4°C for 15 min. The supernatant part of the centrifuge tube was collected as a total protein solution. After the proteins were separated on SDS-PAGE gels and transferred to polyvinylidene fluoride membranes, the membranes were incubated with first antibodies of rabbit calpain two polyclonal antibody (dilution of 1:1500), rabbit SELK polyclonal antibody (dilution of 1:1500), and rabbit anti-βactin (dilution of 1:5000) respectively, followed by the incubation of secondary antibodies of HRP-conjugated goat anti-rabbit antibody (dilution of 1:200). The membranes were treated with the ECL reagents (Tanon, Shanghai, China) and visualized using a Tanon 5200 Multi Chemiluminescent System (Tanon).

Statistical Analysis
SPSS 19.0 software was used, and means between two treatments were compared by the Student's t-test, or means among three or more treatments were compared by one-way ANOVA followed by the least significant difference test. Data were represented as the means ± SEM.

The Escape Latency of Mice
The escape latency of the APP/PS1 model group was not changed significantly after every day's training and learning; on the other hand, that of their control mice group (normal mice) was significantly shortened, indicating that their positioning learning and memory abilities decreased significantly ( Figure 1A). However, the escape latency of the BBMHtreated AD mice group was gradually shortened and approached to control mice after 3 days of training, indicating that the positioning learning and memory abilities of AD mice were greatly improved after BBMH administration. It also could be found that the escape latency of the Alu-Gal AD mice group ( Figure 1B) and Alu AD mice group ( Figure 1C) did not change significantly after 3 days of training and learning, indicating that the learning and memory abilities of them also decreased significantly. However, the escape latency of the BBMH-treated Alu-Gal mice group ( Figure 1B) and Alu mice group ( Figure 1C) was gradually shortened and approached to the control mice group (and/or sham operation group) on the 4th day, indicating that the learning and memory abilities of BBMHtreated Alu-Gal AD mice and Alu AD mice were greatly improved after BBMH administration.

The Time in the Target Quadrant and the Number of Crossing Platform of Mice
After 4 days of learning and training, the time in the target quadrant and the number of crossing platform of APP/PS1 mice, Alu-Gal AD mice, and Alu AD mice were significantly lower than those of their control mice groups on the 5th day ( Figure 2 and Figure 3), indicating that the spatial learning and memory abilities of the three kinds of AD model mice were significantly decreased. After BBMH administration, the time in the target quadrant and the number of crossing the platform of the three kinds of BBMH-treated AD mice groups were significantly increased (Figure 2 and Figure 3), indicating that BBMH could significantly improve the learning and memory abilities of the three kinds of AD mice.

The Effects of BBMH on the Formation of Aβ Deposition in the Brain
Aβ plaque appeared in the hippocampal tissues of the APP/PS1 model mice group and the Alu-Gal AD model group (Figure 4). On the other hand, there was no Aβ plaque in the hippocampal tissues of mice in the control groups. Only few Aβ plaques could be observed in the brain hippocampal tissues of the BBMH group, suggesting that BBMH can inhibit the production of Aβ deposition in the brain hippocampal tissues of APP/PS1 mice. No Aβ plaque was discovered in Alu AD model mice, suggesting that the stereotactic injection of aluminum chloride into the brain can only produce AD-alike model mice since no Aβ deposition appeared.

The Effects of BBMH Administration on the Hippocampal Pyramidal Cells
The pyramidal cells (neurons) in the hippocampal tissues of mice in the control mice groups (normal mice for APP/PS1 mice and Alu-Gal AD mice and sham operation mice for Alu AD mice) of three kinds of AD model mice were in normal shape, orderly arranged, and uniformly colored, without pathological features ( Figure 5). The pyramidal cells in the hippocampal tissues of the three kinds of AD model mice groups showed more lesions, irregular cell boundaries, large intercellular space, and dark red staining after lysis. In all of the three kinds of BBMH-treated AD mice groups, only a few pyramidal cells showed irregular shape, indicating that BBMH can alleviate the hippocampal neuron damage of hippocampal tissues in AD mice.   As shown in Figure 6, no neurofibrillary tangles (NFTs) appeared in the hippocampal tissues of the control mice groups in the three kinds of AD model mice ( Figure 6). On the other hand, many brown neurofibrillary tangles appeared in the hippocampal tissues of three kinds of AD model mice groups. Among them, most brown neurofibrillary tangles appeared in the hippocampal tissues of APP/PS1 model mice, followed by Alu-Gal AD model mice, and least for Alu AD model mice, indicating that APP/PS1 model mice had the highest degree of neuropathy. After BBMH was administered to three kinds of AD mice, the neurofibrillary tangles in the hippocampal tissues of three kinds of BBMH-treated mice groups were significantly reduced. The neurofibrillary tangles completely disappeared in BBMH-treated Alu AD mice and Alu-Gal AD mice, and only a small number of neurofibrillary tangles could be found in BBMH-treated APP/PS1 mice. These results indicate that the neurofibrillary tangles in the hippocampal tissues of AD mice can be reduced by BBMH administration.

The Effects of BBMH on the Expression of Proteolytic Enzyme, Calpain
Since BBMH belongs to bisbenzylisoquinoline alkaloids, which are Ca 2+ channel antagonists (Meng et al., 2019), the expression level of Ca 2+ -dependent proteolytic enzyme, calpain, in the brain tissues of the mice was investigated by Western blot. It was shown that the expression level of calpain in three kinds of AD model mice was significantly increased compared to that of their control mice and that in three kinds of BBMH-treated AD mice was significantly decreased (Figure 7).

The Effects of BBMH on the Expression of Selenoprotein SelK
The expression of selenoprotein SelK, one of the proteolytic target proteins of calpain , in the brain tissues of the mice was also tested by Western blot. It was shown that the expression level of selenoprotein SelK in three kinds of AD model mice was significantly decreased compared to that of their control mice and that of SelK was significantly increased in three kinds of BBMH-treated AD mice (Figure 8).

DISCUSSION
Bisbenzylisoquinoline (BBI) alkaloids belong to the very large isoquinoline alkaloid family, and everyone consisted of two benzylisoquinoline parts connected by oxygen bridges. Most BBI, including fangchinoline, cyclanoline, dauricine, liensinine, isoliensinine, and neferine, exhibited good inhibitory activities of acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) and also demonstrated noncompetitive enzyme inhibition (Lin et al., 2013;Jung et al., 2015;Li et al., 2019;Mir et al., 2021). Microglial cells play an important role in brain tissues through mediating neuroinflammation in Alzheimer's disease (AD) by the production of a series of proinflammatory mediators as well as clearance of Aβ peptides and senile plaques. It has been found that tetrandrine decreases the expression of proinflammatory mediators such as IL-1β and TNF-α in microglial cells by inhibition of NF-κB activation (He et al., 2011). Dauricine can significantly improve cognitive impairments in 3xTg-AD mice by decreasing Aβ plaques and hyperphosphorylated Tau and increasing the hippocampal ATP level by modulating the tricarboxylic acid cycle, synaptic vesicle cycle, glycolysis, and gluconeogenesis in 3xTg-AD mice (Chen Y. et al, 2021). It is proposed that most of the BBI compounds play physiological roles partly by the inhibition of the increasing of the Ca 2+ level in the cells and the subsequent phosphorylation of CaMKII as calmodulin-binding agents (Meng et al., 2019).
Thus, BBI is a multi-target anti-Alzheimer's disease compound based on the aforementioned analysis. In this study, BBMH exhibited significant anti-Alzheimer's disease functions possibly through its multi-target actions. BBMH can also inhibit the expression of Ca 2+dependent proteolytic enzyme, calpain, by regulating the intracellular calcium homeostasis, and increase the expression of SelK, a special selenoprotein expressed mainly in immune cells, thereby enhancing the clearance abilities of Aβ and Tau protein in brain tissues through increasing the phagocytosis of microglial cells and macrophages migrated to the brain.

DATA AVAILABILITY STATEMENT
The original contributions presented in the study are included in the article/Supplementary Materials; further inquiries can be directed to the corresponding author.

ETHICS STATEMENT
The animal study was reviewed and approved by the Institutional Animal Ethics Committee of Liaoning University.

AUTHOR CONTRIBUTIONS
CC performed experiments and wrote the manuscript. H-LW, FQ, and M-HL performed experiments and analyzed data. X-XG and JL helped organize pictures and revise the manuscript. J-HH and N-ZZ helped perform animal feed and experiments. ZX was responsible for overall supervision.


Discerning the role of polymyxin B nonapeptide in restoring the antibacterial activity of azithromycin against antibiotic-resistant Escherichia coli

Antimicrobial resistance is a global public health threat. Antibiotic development pipeline has few new drugs; therefore, using antibiotic adjuvants has been envisioned as a successful method to preserve existing medications to fight multidrug-resistant (MDR) pathogens. In this study, we investigated the synergistic effect of a polymyxin derivative known as polymyxin B nonapeptide (PMBN) with azithromycin (AZT). A total of 54 Escherichia coli strains were first characterized for macrolide resistance genes, and susceptibility to different antibiotics, including AZT. A subset of 24 strains was then selected for synergy testing by the checkerboard assay. PMBN was able to re-sensitize the bacteria to AZT, even in strains with high minimum inhibitory concentrations (MIC: 32 to ≥128 μg/ml) for AZT, and in strains resistant to the last resort drugs such as colistin and meropenem. The fractional inhibitory concentration index was lower than 0.5, demonstrating that PMBN and AZT combinations had a synergistic effect. The combinations worked efficiently in strains carrying mphA gene encoding macrolide phosphotransferase which can cause macrolide inactivation. However, the combinations were inactive in strains having an additional ermB gene encoding macrolide methylase which causes ribosomal drug target alteration. Killing kinetics study showed a significant reduction of bacterial growth after 6 h of treatment with complete killing achieved after 24 h. Transmission electron microscopy showed morphological alterations in the bacteria treated with PMBN alone or in combination with AZT, with evidence of damage to the outer membrane. These results suggested that PMBN acted by increasing the permeability of bacterial outer membrane to AZT, which was also evident using a fluorometric assay. Using multiple antimicrobial agents could therefore be a promising strategy in the eradication of MDR bacteria. PMBN is a good candidate for use with other antibiotics to potentiate their activity, but further studies are required in vivo. This will significantly contribute to resolving antimicrobial resistance crisis.

Introduction
Antimicrobial resistance (AMR) is a global public health crisis (Murray et al., 2022). The World Health Organization (WHO) considered multidrug-resistant (MDR) Gram-negative bacteria (GNB) as a serious threat forebodes returning to a pre-antibiotic era. In the era of coronavirus disease 2019 (COVID-19), managing infections is considered a top priority, especially that this pandemic has been accompanied by increased consumption of antibiotics (Lucien et al., 2021). Limited therapeutic agents are currently effective in treating infections caused by MDR bacteria; therefore, new therapeutic strategies are required (Hamad et al., 2019). It is feasible to further investigate synergistic treatments in solving the AMR crisis, as combinations of multiple antimicrobial agents have proven to be effective; for example, multidrug therapy for managing sepsis caused by carbapenem-resistant Enterobacteriaceae in critically ill patients (Ahmed et al., 2014). The combination of different drugs offers many advantages over the use of a single agent. These advantages include dosage reduction, lower risk for AMR, synergistic effects and the ability to attack multiple bacterial targets simultaneously, leading to an enhanced antibacterial effect (León-Buitimea et al., 2020).
Enterobacterales (including Escherichia coli) are among the most common GNB that are resistant to many antibiotics (Stapleton et al., 2020). E. coli is a member of the normal intestinal microbiota in humans (Kaper et al., 2004). It is responsible for a variety of diseases in both community and hospital settings, associated with high rates of morbidity and mortality (Wu et al., 2021). E. coli is the single most important causative agent of urinary tract infections (UTI), accounting for more than 80% of the UTI episodes (Foxman, 2010). Besides, it can cause a variety of diseases such as bloodstream infections, gastrointestinal infections, sepsis and meningitis (Kaper et al., 2004). Azithromycin (AZT) is a macrolide antibiotic which inhibits bacterial protein synthesis (Parnham et al., 2014). Macrolides, and AZT in particular, have a number of reported non-bactericidal properties that could further complement their antibacterial efficacy. AZT is touted to have anti-inflammatory and immunoregulatory properties through modulation of innate and adaptive immune responses (Zimmermann et al., 2018); thus, it has been used to treat chronic inflammatory disorders of the respiratory system (Zarogoulidis et al., 2012). Furthermore, it has antiviral properties with proven in vitro activity against several viruses like rhinovirus (Schögler et al., 2015) and influenza virus (Tran et al., 2019). AZT antiviral activity was attributed to its ability to reduce the viral entry into the host cells (Tran et al., 2019;Du et al., 2021), and through indirect mechanisms relying on its anti-inflammatory activity (Mohanta et al., 2020;Poddighe and Aljofan, 2020).
In general, macrolides have low levels of activity against Enterobacterales which have been linked to the poor membrane penetration (Gomes et al., 2017). Several studies suggested that AZT has synergistic properties in combination with other antimicrobial agents on GNB (Fernández-Cuenca et al., 2003;Wu et al., 2017), including E. coli (Li et al., 2018). In the latter study, AZT was used in combination with colistin, which acts by destroying the outer membrane of GNB, thereby increasing the amount of AZT in the cytoplasm. A major limitation of the previous studies is the lack of correlation with the genomic properties of the bacteria, as genes responsible for resistance to macrolide were not tested. Some studies reported that colistinbased combination therapy may increase toxicity in the kidneys and liver (Bassetti et al., 2008); thus, more safe alternatives are needed. One of the derivatives of polymyxin is polymyxin B nonapeptide (PMBN), a cationic cyclic peptide derived by enzymatic processing from the naturally occurring peptide polymyxin B. Like colistin, it can increase the permeability of the outer membrane (OM) of GNB toward hydrophobic antibiotics probably by binding to the bacterial lipopolysaccharide (LPS; Tsubery et al., 2000). PMBN is less toxic, lacks bactericidal activity, but is thought to retain its ability to render GNB susceptible to several antibiotics by permeabilizing their outer membranes (Vaara, 2013). Previous studies have demonstrated success in combining polymyxin derivatives with other antibiotics, but mechanistic studies or correlation with genotype of the tested strains were not reported (Vaara, 2019). This study aimed to test the possibility of enhancing the antibacterial activities of AZT in combination with PMBN by investigating their effect on a group of E. coli strains characterized for macrolide resistance genes. The study also aimed to determine the killing kinetics and to examine the effect of the combinations on the outer bacterial membrane with detection of the ultrastructural changes in the treated bacterial cells by the use of transmission electron microscopy.

Bacterial strains
In this study, a total of 54 E. coli strains were investigated, including three bacterial strains obtained from the American Type Culture Collection (ATCC) purchased from Microbiologics, United States, namely, ATCC BAA-2469, CDC AR-0346, and ATCC 25922. In addition, 51 clinical strains isolated from patients attending to Tawam Hospital, Al-Ain, UAE were tested (Supplementary Table S1). Bacterial identification was performed using VITEK 2 system. All isolates were preserved in brain heart infusion broth (MAST, United Kingdom) containing 20% glycerol and stored in −80°C freezer. The strains were checked for purity before any experiment.

Antibiotic susceptibility testing
Antibiotic susceptibility testing was performed according to the Clinical Laboratory Standards Institute (CLSI) guidelines using E. coli ATCC25922 as a quality control (Clinical and Laboratory Standards Institute, 2020). Disk diffusion test was used for the assessment of susceptibility to amoxicillin/clavulanate, Based on antibiotic susceptibility profiles, the strains were classified as multidrug resistant (MDR) if they were resistant to at least one antibiotic in three or more antimicrobial categories (Magiorakos et al., 2012), and extensively drug resistant (XDR) if they were resistant to one antibiotic in ≥6 antimicrobial categories (Saderi and Owlia, 2015).

Checkerboard synergy assay
To evaluate the synergism between PMBN and AZT, checkerboard assay was implemented. Synergy testing was carried out in 96-well microtiter plates on an initial inoculum of 5 × 10 5 CFU/ml using a 7 × 5 well configuration with a final volume of 100 μl (Antonello et al., 2021). AZT was serially diluted starting from a concentration which is two times higher than the MIC (if the bacteria was susceptible), and at 128 μg/ml if the strain was resistant. To date, breakpoints for PMBN as an antibacterial agent are not available, so 32 μg/ml was tested as the maximum dose in the checkerboard assay. MICs of the drugs alone and in combination were determined as the lowest drug concentration inhibiting bacterial growth following overnight incubation at 37°C. As a measure of synergy, fractional inhibitory concentration index (FICI) was calculated. Though PMBN was not active as an antibiotic, its MIC was considered as 128 μg/ml (highest concentration tested) for FICI calculation for most of the strains except those with determined MIC. This was done by other investigators if the tested compounds were not active when used alone (Yarlagadda et al., 2020). The combination was considered synergistic when the FICI was ≤0.5, no interaction occurred when the FICI was >0.5 and ≤ 4, and an antagonistic effect was considered when the FICI was >4 (Rattanapanadda et al., 2019).

Time-kill study
Based on the checkerboard assays, the combinations showing synergy were tested for their killing kinetics in five selected strains (White et al., 1996). Time-kill curves were used to monitor bacterial growth and death over a wide range of antimicrobial concentrations. Bacteria were grown overnight in Trypton Soy agar (TSA) at 37°C. Bacterial cultures in saline were diluted in Mueller-Hinton broth (Oxoid, United Kingdom) to an inoculum of 5 × 10 5 CFU/ml. Drugs were applied as described for the checkerboard assay. Time-kill curves were obtained by removing aliquots from wells with the AZT/PMBN combinations and those with a single agent applied to the bacterial strains grown in a 96-well plate. The plates were incubated at 37°C, and aliquots were collected at different time points (0, 2, 4, 6, and 24 h). At each time point, an inoculum of 5 μl was taken from each well and 10-fold serially diluted in 0.85% NaCl. Immediately after the dilution, 5 μl drop was pipetted on TSA plate and incubated at 37°C overnight for the enumeration of colony forming units (CFU) in each combination (Wu et al., 2019). The detection limit of the assay was 200 CFU/ml, if a minimum growth of one colony was obtained on a TSA platted with the least sample dilution (5 μl aliquot from 100 μl culture). A CFU of 0 was reported if no growth was observed from the least dilution tested. The procedure was performed in duplicate (two independent experiments) for each strain using round bottom microwell plates (Sarstedt, Germany) for proper mixing during dilution. A graph was plotted using log 10 bacterial concentration at each time point for each concentration tested. Log 10 CFU/ml was plotted on the Y axis against time on X axis (Hamad et al., 2022).

Outer membrane permeability
The outer membrane permeation activity of the PMPN and combinations was assessed by the 1-N-phenylnaphthylamine (NPN) assay (Sigma, United States), as described previously with Frontiers in Microbiology 04 frontiersin.org slight modifications (Zhou et al., 2019). Briefly, mid-logarithmic phase bacterial cells adjusted to a density of 10 9 CFU/ml (equivalent to 3.0 MacFarland) were added to black 96 well microplates containing 10 μM NPN and colistin, PMBN or AZT (serial dilutions), or a combination of PMBN and AZM (at FICI). The plates were incubated for 1 h at 37° C protected from light. Fluorescence intensity was measured after 1 h using infinite M200 PRO fluorescence microplate reader (Tecan, United States) at 350 nm excitation and 420 nm emission wavelengths. NPN uptake (%) was calculated for each strain as described before (MacNair et al., 2018), as follows: Where F obs is the observed fluorescence at a given concentration of the drug, F 0 is the initial fluorescence of NPN with E. coli cells in the absence of any treatment, and F 100 is the fluorescence of NPN with E. coli cells upon addition of 128 μg/ml of colistin, as full NPN uptake (100%) was reported to be achieved at high concentrations of colistin (MacNair et al., 2018;Zhou et al., 2019).

Transmission electron microscopy
Transmission electron microscopy (TEM) was used to look for changes in cell morphology of a selected bacterial strain (EC26) after treatment with the test agents. The procedure followed as described before with some modifications (Nguyen et al., 2021). A single colony from an overnight culture was suspended in 10 ml LB broth (Invitrogen, United States), and incubated with shaking at 37°C overnight. The overnight culture was diluted 1:30 in 40 ml LB broth and kept under continuous agitation for 2 h, adjusted to OD 600 of 0.5 (approximately 4 × 10 8 CFU/ml). The bacteria were treated with either AZT (8 μg/ ml); PMBN (16 μg/ml) or a combination of PMBN and AZT (16 and 8 μg/ml, respectively). Untreated culture was used as a growth control (negative control) and culture treated with colistin (2 μg/ ml) was used as a positive control. Both treated and untreated cultures were incubated for 1 h with shaking at 37°C. The cells were harvested by centrifugation at 4°C to avoid cell damage, washed and resuspended in 0.1 M phosphate-buffered saline (PBS; pH 7.2). Cells were fixed overnight with a fixative containing 4% formaldehyde, 1.25% glutaraldehyde, 4% sucrose, 0.01 M CaCl 2 , 0.075% ruthenium red in PBS. The fixed cells were washed three times in PBS buffer, post fixed in 1% osmium tetroxide in PBS containing 0.075% ruthenium red for 1 h, and washed with water afterwards. Cells were dehydrated with ascending ethanol grades (30,50,70,80,90,95, and 100%) each for 15 min, and finally treated with propylene oxide. The cells were infiltrated for 1 h each in propylene oxide: Agar 100 epoxy resin in ratio of 1:1, 1:2, and 1:3 and polymerized at 65° C for 24 h. Blocks were trimmed and then semithin and ultrathin sections were cut with EM UC7 Ultracuts ultramicrotome (Leica, Vienna, Austria). Semithin sections (1.5 μm thickness) were collected and stained with 1% aqueous toluidine blue on glass slides with a hot plate at 70°C. Ultrathin sections (95 nm) with gold color were collected on 200 mesh copper grids, air dried and then were contrasted with uranyl acetate followed by lead citrate. Then, grids were examined with transmission electron microscope (Tecnai Biotwin Spirit G2, The Netherlands) and images were taken at 80 Kv at different magnifications.

Statistical analyses
All statistics were performed using IBM SPSS (version 26, SPSS Inc., Chicago, IL, United States). Graphs were generated using GraphPad Prism ® Version 9.0 (GraphPad Software, Inc., La Jolla, CA, United States). Kruskal-Walli's rank sum test and Mann-Whitney U-tests were performed to compare the groups as appropriate. Statistical significance was determined at p < 0.05.

Results
Except the quality control strain (ATCC 25922), all the E. coli strains tested in this study were highly resistant to multiple antibiotics (MDR), including strains that were also resistant to the last resort drugs including carbapenems (six strains with bla NDM gene and two strains with bla OXA-48 like gene), and colistin (one strain with mcr-1 gene; CDC AR-0346). Results of antibiotic susceptibility testing and resistance genes detected in the strains are shown in the Supplementary Table S1.
After defining the properties of the strains, a subset was selected for synergy testing including 24 strains with different MICs for AZT and different profiles of resistance genes. As shown in Table 1, combinations of PMBN and AZT acted in synergy (FICI < 0.5) in 16 strains including 12 strains with mphA gene alone. On the other hand, no synergy between the AZT with PMBN was noted in the presence of ermB with mphA in all the eight tested strains.
The results of the checkerboard synergy assays are presented in Supplementary Table S2, which shows the results obtained from the 16 strains on which AZT and PMBN combinations were synergistic, and fold reduction in AZT MIC when used in combination with different PMBN concentrations. Supplementary Table S3  In all the combinations, there was a significant decrease in the MICs of AZT, with at least 6 folds reduction (p < 0.05). When the mean MICs of AZT in the combinations were compared, they were not significantly different when different concentrations of PMBN were applied (p > 0.05), although the highest foldreduction in MIC (9.1-16 folds) was found when the highest concentration of PMBN (32 μg/ml) was used. However, when strains with different MICs for AZT (4-≥ 128 μg/ml) were compared, a significant difference in the MICs in the combinations was found (p < 0.05), as strains with lower MICs for AZT (4-8 μg/ ml) demonstrated better response to treatment with AZT and PMBN (mean MIC: 0.4-1.5 μg/ml; mean folds reduction in MIC: 6-12) compared to strains having high AZT MICs (32 to ≥128 μg/ ml) exhibiting mean MIC of 3-16 μg/ml (mean folds reduction in MIC: 5.7-16) when treated with both AZT and PMBN (data shown in Supplementary Table S3, and folds reduction in MICs are shown in Figure 1).
Time kill assays were performed for five selected E. coli strains, in order to determine the time required to achieve bacterial killing by the synergetic combinations. Different combinations of AZT with PMBN were tested. Multiple combinations significantly reduced the growth during the first 6 h of treatment with complete killing achieved after 24 h of treatment in all the tested strains. Notably, all the bacterial strains were highly resistant to PMBN when used alone, and to AZT when used in sub-inhibitory concentrations. Figure 2 is a representative time-kill graph for a selected strain (CDC AR-0346), while time-kill graphs of the other four strains are shown in the Supplementary Figures 1-4. The sole effect of AZT on bacterial growth was tested in one strain (EC26), and the results showed that AZT was not able to kill the bacteria at concentrations below the MIC (Supplementary Figure 4F); thus, the experiment was not attempted on more strains.
As shown in Figure 2, combinations of PMBN and AZT caused significant reduction in the bacterial growth after 6 h of treatment and complete killing after 24 h. At high concentration of PMBN (32 and 16 μg/ml; Figures 2A,B), combination with AZT (> 4 μg/ml) caused bacterial killing, while lower concentrations of PMBN (8, 4 and 2 μg/ml; Figures 2C,E), in combination with higher concentration of AZT (> 8 μg/ml) were required to achieve the same effect. When applied alone, PMBN at the highest tested concentration (32 μg/ml) had no effect on the bacteria with a growth pattern similar to the untreated growth control (Figures 2A-E). The same pattern of killing was observed in the other strains ( Supplementary Figures 1-4) as PMBN concentration of 16-32 μg/ml caused killing at AZT concentration 2 folds higher than the bactericidal concentration used with 2-8 μg/ml of PMBN. As shown in Figure 3 which summarizes the NPN assay results, PMBN was able to increase the permeability of the outer membrane in a concentration-dependent manner ( Figure 3B), as also shown for colistin ( Figure 3A) based on NPN uptake (%) after treatment with various concentrations of the drugs. AZT alone did not have any effect on the outer membrane as mean ± SD of % NPN uptake was 0.53 ± 0.3 (as shown in Figure 3C, for selected strains). Based on NPN uptake (%) results, combination of PMBN and AZT increased outer membrane permeability which was slightly less than using PMBN alone; however, the difference was not significant ( Figure 3C).
It is important to note, that one strain (CDC AR-0346) which was resistant to colistin, demonstrated the least response to PMBN, compared to the other strains. Nevertheless, synergistic combinations of PMBN and AZT had bactericidal effects as shown in the time-kill study ( Figure 2). As for strain CDC AR-0346, FICI (PMBN/AZT = 4/8 μg/ml) was able to lower bacterial growth significantly without killing based on the results of the time-kill study shown in Figure 2D, as the damage to the outer membrane was low (less NPN uptake) as seen in Figure 3C.
Transmission electron microscopy was used to examine the ultrastructural changes in a selected strain (EC26) treated with the synergistic combination compared to a single agent. As shown in Figure 4, untreated bacteria (A) and bacterial cells treated with AZT (B) had intact outer membrane (OM) and a clear periplasmic space (PS). One the other hand, bacteria treated with the control antibiotic colistin (C) had disrupted OM with threads detached due to the damage. PMBN also compromised the integrity of the OM when used alone (D) as well as in combination with AZT (E). This was accompanied by loss or reduction of the periplasmic space underneath the OM. Interestingly, cells treated with PMBN alone or in the combination demonstrated alterations in the cellular morphology with loss of the characteristic rod shapes and smooth well-defined OM as seen in many cells, shown in Figure 5. Protrusions or blebs were also detected on the surface of treated E. coli as shown in Figures 4D-E, 5A-D demonstrating the damage to OM in a strain (EC26) treated with PMBN alone and in combination with AZT.

Discussion
The emergence of AMR coupled with the slow development of new antimicrobial drugs represent a growing worldwide challenge for infection management which is now a top priority in the era of COVID-19 pandemic (Bendala Estrada et al., 2021). One strategy to tackle the AMR problem was thought to be by teaching old antibiotics new tricks to continue their use and preserve effectiveness against MDR bacteria (Deshayes et al., 2016). Thus, one of the approaches to fight MDR bacteria is the discovery of potent synergistic combinations, which could be implemented without the need to modify existing drugs (León-Buitimea et al., 2020). In this study, we have proven the effectiveness of synergistic combinations composed of AZT and PMBN. These combinations were found effective on MDR strains of E. coli, even those resistant to the last resort drugs including carbapenems and colistin. The spread of resistance to the latter drugs increased the need for alternatives to these medications to fight highly resistant bacteria (Janssen et al., 2020;Humphrey et al., 2021).
Synergy is commonly found in combinations utilizing drugs that target the bacterial membrane and antibiotics that target the biosynthesis of nucleic acids and proteins (Zharkova et al., 2019). Thus, a membrane targeting agent (PMBN) and a protein synthesis inhibitor (AZT) were selected for use in this study. AZT was selected for use in the synergetic combinations, as AZT is recognized as one of the effective weapons to fight microbial infections, augmented by its anti-inflammatory and immuno modulatory effects (Zimmermann et al., 2018). A recent multicenter study in Spain, analyzed the use of antibiotics in patients hospitalized due to SARS-CoV-2 infection. Higher mortality rates were reported with use of all antibiotics except macrolides, particularly AZT, which caused a significantly higher survival rate among COVID-19 patients (Bendala Estrada et al., 2021). It is important to note that AZT belongs to the parent class of macrolides that are characteristically bacteriostatic, with some evidence suggesting that bactericidal killing can be achieved through concentration-dependent effects (Dorfman et al., 2008). We have noticed the same in our study, as high concentrations of AZT were required to kill the bacteria when used alone, as seen in Time-kill graphs for a selected strain (E. coli CDC AR-0346) tested with different synergistic combinations of PMBN (2-32 μg/ml) and AZT (4-32 μg/ml). A single agent (AZT or PMBN) at a concentration of 32 μg/ml were used as controls in addition to an untreated growth control (GC). AZT ≥ 8 μg/ml killed the bacteria in all the synergistic combinations with PMBN. Mean of duplicates from two independent experiments ± SD are shown.
Frontiers in Microbiology 08 frontiersin.org the time-kill assays. Importantly, bactericidal effects of the combinations were obtained when concentrations lower than the MIC of AZT were used, indicating the success of the combinations to lower the dose of the drug. Previous studies have demonstrated that AZM entry and activity against MDR-GNB was synergistically enhanced when the bacterial outer membrane is perturbed by other molecules such as cationic human antimicrobial peptide (LL-37) or by the last-line antibiotic colistin (Lin et al., 2015). However, it is important to mention that the latter two drugs have antibacterial activities when used alone as reported by several investigators (Humphrey et al., 2021;Ridyard and Overhage, 2021). Added to that, colistin is well known for its side effects, such as nephrotoxicity and neurotoxicity (Poirel et al., 2017), which might be a factor for considering other alternatives with less side effects. The combination of LL-37 and AZT was also shown to increase the permeability of MDR-GNB, by initiating a positive feedback loop that increases the active intracellular levels of AZT (Lin et al., 2015). NPN uptake (%) of five selected E. coli strains. Data shown represent NPN uptake by bacteria treated with serial dilution (128-0.5 μg/ml) of colistin used as a positive control (A) and PMBN (B), in addition to bacteria treated with synergetic combination of PMBN and AZT at FICI compared to treatment with a single agent (C). NS is non-significant.
Frontiers in Microbiology 09 frontiersin.org Although polymyxin derivatives have been used to sensitize GNB to other antibiotics (Vaara, 2019), PMBN was not widely used in combination with other drugs, probably due to its weak antibacterial effects when used alone (Ferrer-Espada et al., 2019). We agree with the previous reports, as all the strains tested in this study expressed low susceptibility to PMBN monotherapy, but was effective when used in combination with AZT. It was reported that colistin can impact the surface integrity of colistin-resistant bacterial strains, maintaining antibiotic potentiation with other drugs in pathogens expressing chromosomally mediated resistance to colistin monotherapy (Vidaillac et al., 2012). A previous study has shown that mcr-1-mediated LPS modification associated with colistin resistance can protect the cytoplasmic but not the outer membrane from damage caused by colistin. Despite OM damage of resistant bacteria, it was unable to kill or lyse them, leading to bacterial survival after treatment with colistin . Furthermore, permeabilization of the OM of colistinresistant strains was sufficient to sensitize bacteria to other antibiotics as rifampicin, which normally cannot cross the OM (Humphrey et al., 2021). Whilst OM permeabilization is crucial for access of colistin to the cytoplasmic membrane, permeabilization of phospholipid bilayers of cytoplasmic membrane is required for the bactericidal and lytic activity of the antibiotic . The same can be applied to PMBN. Although all the strains tested in this study expressed high MICs to PMBN (64-≥128 μg/ml) indicating that the molecule has no bactericidal effect when used alone, it was obvious that the molecule can perturb the integrity of the bacterial OM as seen by TEM. Morphological changes caused by exposure to PMBN were observed by other investigators using scanning electron microscopy (Dixon and Chopra, 1986). As observed in this study, PMBN induced numerous protrusions or blebs on the surface of E. coli. However, PMBN failed to cause leakage of the cytoplasmic components and the damage was confined to the OM, as reported before (Dixon and Chopra, 1986).
Furthermore, studies with 1-N-phenylnaphthylamine (NPN), a validated marker for outer membrane permeability of GNB verified that PMBN can increase outer membrane permeability in E. coli (Helander and Mattila-Sandholm, 2000). NPN is a hydrophobic fluorophore, which acts as a sensitive probe for OM barrier function. It can be excluded from the OM of intact cells, but if the OM is permeabilized, NPN enters the cells and emits fluorescence in the hydrophobic interior of the membrane, in a concentration-dependent manner (Akhoundsadegh et al., 2019). The same fluorescence pattern was noticed in this study as NPN fluorescence (% uptake) was more in the presence of high Frontiers in Microbiology 10 frontiersin.org concentrations of PMBN and was reduced at low doses of the molecule. This dose-dependent effect can explain why PMBN produced potent bactericidal effects at high concentration (16-32 μg/ml) requiring lower doses of AZT to achieve bacterial killing, while it was less potent when applied at lower doses (2-8 μg/ml) requiring higher AZT concentrations to achieve the same effect as observed in the time-kill studies. Based on the results, it is evident that PMBN acted by increasing the permeability of the bacterial membrane facilitating the entry of AZT into the cell where it can exert its antimicrobial effects. Furthermore, AZT uptake was affected by the extent of OM damage induced by PMBN which is dose dependent. It was noticed that strains with low MICs of AZT demonstrated better response to treatment with the combination compared to strains with high MICs. This is related to the genomic content of the strains. It is important to mention that more than half (62.96%) of the bacteria tested were resistant to AZT due to the presence of mphA with or without ermB. In this study, synergetic combinations were not effective in the presence of ermB gene. This gene encodes a methylase that confers macrolide-lincosamide-streptogramin B resistance. Methylation of the ribosomal target of the antibiotics by ermB enzyme causes resistance due to drug target alteration, resulting in the inability of AZT to interact with ribosome (Leclercq, 2002;Gomes et al., 2017). Thus, even if AZT is present in high concentration in the bacterial cytoplasm, it will not bind to the methylated target on the bacterial ribosome; thus, it could not inhibit protein synthesis. On the other hand, synergistic combinations were successful in the presence of mphA gene alone. This gene encodes the macrolide 2′-phosphotransferase I (MphA) which is an enzyme that causes inactivation of macrolides (Leclercq, 2002). It has been identified as a cause of resistance in E. coli (Xiang et al., 2020) and reported to be the most common gene associated with AZT resistance (Gomes et al., 2019), as was also observed in this study. A possible explanation on how the synergistic combinations were still effective in the presence of mphA gene is that when the intracellular concentration of AZT was high due to the OM permeabilization by PMBN facilitating drug entry, AZT was able to overpower the enzyme produced by the bacteria to inactive the drug. This effect may be augmented by the damage induced by PMBN, confirming the role of the synergistic combinations in causing bacterial death which is not achievable using a single agent.
It is noteworthy that ermB containing strains had higher AZT MICs (>128 μg/ml) than majority of mphA harboring strains (MIC: 32 to >128 μg/ml), which could affect the likelihood of success of synergy with PMBN. Unfortunately, the sole effect of ermB gene could not be explored as none of the strains tested in this study possess this gene alone, which is a common limitation encountered in previous studies (Tuan-Anh et al., 2020). The latter study reported that strains harboring both ermB with mphA genes had significantly higher MICs than strains without ermB gene, in agreement with our results. Moreover, Tuan-Anh et al. found that amongst the E. coli strains carrying mphA gene, strains with additional ermB gene demonstrated lower growth rate than strains devoid from this gene, which was attributed to reduced ribosomal synthesis in the presence of ermB gene due to methylation of 23S rRNA with potential interference with ribosome assembly. These observations support our conclusion that ermB gene has a substantial impact on bacterial susceptibility to AZT; therefore, it was associated with higher MICs and lack of response to the synergistic combinations. Furthermore, our results shed the light on the challenges facing the use of synergistic combinations on strains expressing multiple resistance mechanisms to the same antibiotic.
Indeed, the feasibility of using the synergistic combinations as therapeutic agents needs in-depth investigations. Combinations constituting of low dose of AZT may have promising clinical applications in treating different types of infections caused by E. coli when the last resort drugs are not effective. Clinically, E. coli is a common cause of various infections like UTIs (Foxman, 2010), bloodstream and gastrointestinal infections (Kaper et al., 2004). It is noteworthy that AZT is commonly used to treat diarrhea caused by some strains of E. coli (DuPont, 2007;Denham et al., 2018) with limited clinical applications in managing other types of infection. The combinations described in this study can expand the clinical spectrum of AZT to include other types of infections caused by E coli, but in vivo studies must be conducted to test the efficacy of these combinations. Clinical data are required to establish susceptibility breakpoints for AZT in E. coli, in order to guide the appropriate use of this antimicrobial agent with valuable biological activities.

Conclusion
In this study, PMBN was able to re-sensitize E. coli to AZT, even in strains with high MICs when the drug used alone. The combinations were effective in the absence of genes that cause drug target alteration. Indeed, these findings highlight the importance of bacterial genotyping before applying therapeutic combinations, as some genes can cause irreversible resistance to certain antibiotics. We have demonstrated permeabilization of the outer membrane of E. coli by PMBN facilitating the entry of AZT molecule. To the best of our knowledge, the use of PMBN in synergy with AZT was not reported before, and as PMBN has good safety profile with less renal toxicity compared with colistin (Brown and Dawson, 2017), it can be tested in combination with other antibiotics, to explore the potential for using it at as an adjuvant to existing antibiotics. Further studies are warranted to evaluate if these combinations can act on other GNB. The same approach can be also used to enhance the entry of drugs with poor penetration into GNB membranes. Ultimately, this should be followed by testing the combinations in vivo. Indeed, synergistic drug combinations will be of added value to the exciting arsenal of antimicrobial agents in the war against MDR bacteria.

Data availability statement
The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.


s2fieldsofstudy: Biology Psychology

text:
Vocal signals facilitate cooperative hunting in wild chimpanzees

Cooperation and communication likely coevolved in humans. However, the evolutionary roots of this interdependence remain unclear. We address this issue by investigating the role of vocal signals in facilitating a group cooperative behavior in an ape species: hunting in wild chimpanzees. First, we show that bark vocalizations produced before hunt initiation are reliable signals of behavioral motivation, with barkers being most likely to participate in the hunt. Next, we find that barks are associated with greater hunter recruitment and more effective hunting, with shorter latencies to hunting initiation and prey capture. Our results indicate that the coevolutionary relationship between vocal communication and group-level cooperation is not unique to humans in the ape lineage and is likely to have been present in our last common ancestor with chimpanzees.

INTRODUCTION
A notable feature of the human species is our proclivity for flexible cooperation on both small and large scales. This is virtually impossible without vocal communication because complex coordination of actions requires individuals to relate to each other in space and time (1,2). A prevailing hypothesis is that human language and cooperation coevolved, with advances in communication facilitating more complex cooperative behavior, which, in turn, selected for more sophisticated linguistic skills (3)(4)(5). Such an interdependence between group-level cooperation and communication seems to have evolved convergently, albeit more simply, in distantly related species including Indo-Pacific bottlenose dolphins (Tursiops aduncus), shedding light on the shared selection pressures driving this social behavior (1,(6)(7)(8). What remains unclear, however, is whether, within the primate order, the coevolution of communication and cooperation is a derived feature of the hominin lineage or whether its basic building blocks may be found in our closest living primate relatives.
There is ample evidence that wild primates produce communicative signals to initiate and regulate dyadic affiliative interactions, such as play, grooming, and reconciliation (9)(10)(11)(12)(13)(14)(15). However, more explicit investigations into dyadic cooperation in captive apes (e.g., tasks to obtain food rewards) have reported either no overt communication between the two partners or the occasional exchange of short-range, nonvocal gestures (2,(16)(17)(18). Furthermore, much of what makes human cooperation extraordinary is the high frequency of polyadic cooperation (i.e., involving more than two individuals) (19). This type of group-level cooperation is evolutionarily less stable than dyadic cooperation due to the emergence of collective action problems. Specifically, an increasing group size dilutes individual payoffs and generates more opportunities to free ride on the efforts of others (20). Therefore, polyadic cooperation might particularly benefit from facilitation by means of overt communication (1,6,8).
Studies of monkeys (and other species) have shown that communicative signals are important in mediating collective predator deterrence such as mobbing, as well as intergroup conflict (21)(22)(23)(24). However, to date, little is known about the coordination of polyadic cooperative behavior in great apes, data that are key to a phylogenetic reconstruction of the intimate link between cooperation and communication in humans.
Group hunting by chimpanzees is an iconic example of polyadic cooperative behavior. In line with a long tradition of chimpanzee hunting research, we define cooperation as a behavior involving multiple individuals, where joint participation increases success (25,1). A basic form of cooperation, known as by-product mutualism, does not require that individuals relate to each other in space and time, but rather involves independent, simultaneous actions that incidentally increase the chances of others' success. By contrast, the term coordination is used to describe a more complex form of cooperation, whereby participants respond to each other's communicative behaviors and movements, thus actively modifying their behavior depending on the actions of others. There is disagreement over the extent to which coordination is involved in chimpanzee group hunting. While some research indicates that chimpanzee hunting represents a case of by-product mutualism, where the actions of participants are effectively independent efforts geared toward individual success (26)(27)(28), other studies suggest that there is coordination involved, including the existence of specific roles and tasks both before and during the hunt, which rely on interdependent action for success (25,29,30). It has recently been suggested that cooperative acts may be best conceptualized as falling on a graded continuum that includes intermediate forms between by-product mutualism and full-blown teamwork (1). Chimpanzee group hunting may vary along this continuum both within and between populations. Some hunts are potentially well described as a socially influenced collaboration, where individuals' actions are influenced by others but no intentional strategies to coordinate are in place (1). This debate notwithstanding, from a descriptive perspective, chimpanzee hunts typically involve the pursuit of arboreal group-living monkeys, most often red colobus monkeys (Procolobus tephrosceles), by multiple chimpanzees (29,31). When one or more monkeys are killed, the captors frequently share meat with others (26). At our research site (Kanyawara, Kibale National Park, Uganda), chimpanzees do not appear to actively search for monkeys [c.f. Ngogo and Taï (32,33)]; instead, they see or hear troops opportunistically as they travel through their range.
When chimpanzees encounter a monkey troop, one or more individuals may immediately climb in pursuit, or there may be a period of minutes to hours during which individuals appear to assess their chances of success by scanning the canopy from the ground. Males are more likely than females to hunt arboreal monkeys (34), and while occasionally an individual will hunt alone, the majority of hunts at most sites involve multiple hunters (35). The probability of a kill increases with the number of active hunters, although there is debate over the per capita benefits of hunting in groups (29,36). Overall, as hunts may unfold over a large forested area with potentially poor visibility, the vocal expression of motivation to hunt would arguably improve the efficacy of group hunts (31). Vocalizations could provide valuable information, such as the number, location, identity, and/or motivation of hunters, which individuals can use to make their own decision about whether to participate.
Chimpanzee bark vocalizations occur in the context of group hunting (31,37), and previous research has shown this vocalization to be acoustically distinct from barks produced in other contexts (37). If acoustic communication is truly tied to the evolution of largescale cooperation as the literature suggests (3)(4)(5), then multiple features render chimpanzee hunting a likely case in which to observe context-specific vocal behavior. First, hunts are energetically costly and associated with variable success and risk of injury (26,34), representing a selective pressure on hunter performance. Next, hunt success is known to be dependent on factors that might be most efficiently detected via vocalizations, such as the number and identity of hunters (26,28,31,36). Last, barks produced in hunting contexts are generally emitted before active pursuit of prey, reflecting a likely communicative function (31). Here, we tested the hypothesis that these "hunting barks" facilitate cooperation by increasing participation or efficacy in hunts. First, we tested the prediction that a bark signals an individual's motivation to hunt by examining the relationship between bark production and hunt participation at the individual level. As hunting success is related to the number and identity of hunters, this information should be useful to individuals who are undecided about whether to hunt. Accordingly, we next predicted that, at the level of hunting events, barks would be associated with increased participation or expedited hunt initiation. Last, we predicted that barks would be associated with increased hunting success in terms of greater kill probability or shorter latency to make a kill. Given the rich social context of hunts, our analyses also controlled for age, sex, and party size and composition ("party" refers to a temporary subgroup of chimpanzees belonging to the same community; see Materials and Methods for the operational definition used).
To study hunting in chimpanzees, specific aspects of this complex behavior must first be operationally defined. Given that it is empirically intractable to unambiguously establish when the chimpanzees themselves perceive the hunt to have started, we instead rely on a criterion that has been frequently applied across multiple field sites, defining the start of a hunt as the time at which the first chimpanzee climbed to the height of the lowest monkey (see Materials and Methods) (28,33). This represents a standardized way to describe the initiation of prey pursuit, a key feature required to designate a hunt as such. We examined the production of barks during a total of 307 hunting events over a 23-year period (1996-2018) in the Kanyawara chimpanzee community of Kibale National Park, Uganda. To exclude ambiguous events, we removed a subset of data consisting of incomplete records and events where the identity of barkers was unknown (n = 75) from further analysis. In the remaining 232 events, we found that barks were produced in 39% of events (91 of 232), with a total of 343 individuals producing barks (mean of 3.7 individuals per event; SD = 2.3; see Materials and Methods for further information about model-specific sample sizes). In 93% of events with barks (85 of 91), barks occurred before the start of the hunt. Barkers started to vocalize, on average, 47 s (SD = 157 s) before hunt initiation, although it was not feasible to record the duration of barking bouts in real time. Henceforth, all references to barking indicate single barks or bouts of barks that commenced before hunt initiation. We classified individuals as hunt participants only when they actively pursued monkey prey, not merely if they produced a hunting bark.
We provide more descriptive details on general hunting dynamics in the Supplementary Materials. We modeled our data in a Bayesian framework, and, where possible, we presented a Bayesian version of the R 2 statistic and summarized its posterior probability distribution by its mean (R 2  ) and associated 95% credible interval (R 2 CI ). We offer biological interpretations for model parameters of fixed effects that do not feature in any higher-order effects, and, for which, the posterior 95% credible interval did not include 0. See Materials and Methods for more details on model specification and validation.

RESULTS
To investigate whether the production of barks was associated with an increased probability of individual participation, we constructed a generalized linear mixed-effects model (binary GLMM) using 2398 observations on 74 chimpanzees across 227 hunting events. The slight variation in sample size among the five different models reflects the availability of response variables for extraction (see Materials and Methods). This model ( elpd ± SE elpd = −212.8 ± 18.9; table S3), which controlled for individual characteristics and social context, explained a substantial amount of variation in the probability of individual participation in a hunting event (R 2  = 0.465, R 2 CI = 0.442 to 0.486). The probability of participation was considerably higher for an individual that had barked than one that had not (b  = 5.70, b CI = 3.80 to 7.76) (Fig. 1). This finding suggests that the bark vocalization used in a hunting context was an honest signal of hunting motivation (31).
In line with previous research (28), our first model also found that hunting probability was quadratically associated with age, with a maximum probability of individual participation at an age of ~25 years (b  quadratic = −0.39, b CI quadratic = −0.55 to −0.23). In addition, as chimpanzee party size increased, the probability that a given individual participated decreased (b  = 0.04, b CI = −0.08 to 0.00), as would be predicted if chimpanzees were encountering a collective action problem (see the Supplementary Materials). We further found sex-specific effects of both the presence of "impact hunters," i.e., individuals whose presence increases the likelihood of hunt occurrence (see the Supplementary Materials for impact male calculation) (26,28) and swollen (sexually receptive) females in the hunting party, with males overall being more likely to participate than females, particularly in the presence of impact hunters (b  = 1.59, b CI = 0.76 to 2.40), but becoming less likely to participate whenever swollen females were present (b  = −0.71, b CI = −1.29 to −0.15).
We then addressed the role of barks in augmenting group participation and expediting hunt initiation and asked whether barks influenced the outcome of a hunt. These analyses were conducted at the level of hunting event rather than individual. We expected events in which barks were produced before hunt initiation to be associated with greater group participation and reduced delay in hunt initiation, compared to events with no barks. We also predicted an increase in the probability of making a kill and a reduction in the delay to make it, in association with bark production.
The first model (binomial GLMM; n obs = 232;  elpd ± SE elpd = −38.2 ± 8.4) accounted for more than half of the variation in the proportion of individuals recruited (R 2  = 0.614, R 2 CI = 0.518 to 0.696). Bark occurrence (i.e., at least one individual barked) was associated with an increase in the proportion of individuals in the party that participated in the hunt (b  = 0.45, b CI = 0.22 to 0.69; Fig. 2A and table S4). We also detected a positive effect of impact hunter presence on the proportion of individuals that participated (b  = 0.37, b CI = 0.03 to 0.71) and a negative effect of party size (b  = −0.11, b CI = −0.13 to −0.08).
We ran a second model to explain the variation in the latency from the first predatory interest in monkeys to hunt initiation (Cox proportional hazards LM; n obs = 230;  elpd ± SE elpd = −0.3 ± 11.5). , whereas when either independent individuals' party size (b  = 0.06, b CI = 0.00 to 0.13) or the number of hunters (b  = 0.19, b CI = 0.03 to 0.35) was larger, kill probability increased, corroborating previous findings (30,31,35,36).
A final model investigated variation in the latency from hunt initiation to the first kill (Cox proportional hazards LM; n obs = 157;  elpd ± SE elpd = −5.3 ± 4.7). Kills occurred more quickly in hunting events preceded by barks than in those without barks (b  = 0.63, b CI = 0.28 to 0.96; Fig. 2D and table S4). We also found that the time to obtain a kill was greater when impact males were present (i.e., the hazard ratio decreased: b  = −0.69, b CI = −1.17 to −0.20). The reasons for this counterintuitive finding demand further investigation, but impact males may be more likely to persevere under difficult conditions rather than to abandon the endeavor, leading to a longer interval between hunt initiation and success. Measuring individuallevel latencies from joining a hunt to perpetrating a kill and comparing impact and nonimpact hunters would contribute to clarifying this.

DISCUSSION
By examining the association between barks and subsequent hunting dynamics in chimpanzees, we provide evidence that vocal signals are associated with more efficient cooperative resource acquisition in a wild ape. Specifically, we show that individuals who barked before hunt initiation could be reliably expected to pursue prey in the hunt. Next, we demonstrate that the occurrence of barks before hunt initiation was associated with (i) a greater proportion of party members participating in the hunt, (ii) a reduced latency to initiate hunting, and (iii) a reduced latency to make a kill.
If bark vocalizations serve to catalyze hunting as our data suggest, then it is pertinent to ask why these vocal signals are produced only in a subset of hunts. Although we do not explicitly test this question, it is likely that hunting bark production is influenced by a number of social and environmental factors. One possibility is that environmental visibility plays a role, with barks being more likely to be produced in settings with low visibility, where vocal communication may be more important to allow hunters to gauge participation and/or maintain cohesion. A targeted study quantifying vegetation density during hunts would be instrumental in evaluating this hypothesis. Another potential predictor of bark occurrence might be favorable conditions for hunting, including, for example, certain prey demographics (e.g., presence of vulnerable juveniles or infant monkeys) and/or canopy structure (e.g., sparse canopy offering fewer escape routes). Increased arousal in the face of these favorable conditions may then induce hunting-specific vocal behavior in chimpanzees.
It is important to consider whether favorable hunting conditions could offer an alternative explanation for our results, acting as a confound that may be independently driving both bark production and hunting success. Although, in correlational analyses, it is impossible to rule out unmeasured confounding factors, it seems unlikely that favorable hunting conditions can explain our findings. Favorable hunting conditions are difficult to objectively quantify, but they should result in a higher likelihood of a successful kill. Critically, if favorable conditions were confounding the relationship between barking and hunting success, then barks should reliably predict the likelihood of a kill. However, kill success was not strongly influenced by bark vocalization production in our study, making it unlikely that our pattern of results was confounded by favorable hunting conditions. Ultimately, future research should strive to move beyond the observational, correlational patterns we found to probe causal relationships through playback experiments (38). For instance, in areas where monkeys are not present, monkey calls could be broadcast in combination with either chimpanzee hunting barks or chimpanzee vocalizations, which do not occur in the hunting context. If barks catalyze hunting responses as the observational data suggest, then playbacks of monkey calls with barks should elicit more sustained hunting preparation behaviors (e.g., canopy-oriented searching) than when monkey calls with nonhunting vocalizations are broadcast.
Previous work has illustrated how the advertisement of behavioral motivation critically underpins active forms of group coordination (1,39). For example, olive baboons (Papio anubis) and wild dogs (Lycaon pictus) base decisions related to group movement on the number of individuals who communicate their readiness to initiate a group departure (40,41). Because the production of a bark vocalization by a chimpanzee, in the presence of monkey prey, was a strong predictor of the individual actively participating in prey pursuit, this vocalization potentially allowed receivers to reliably assess the extent of participation and, thus, the costs and benefits associated with hunting. Increased participation has previously been shown to be associated with greater success (26) and may potentially reduce individual risks (e.g., attacks from male monkeys), so assuming that receivers are sensitive to these factors, they may be more motivated to hunt when barking occurs. In line with a previous observation (31), we detected a positive relationship between barks and subsequent participation, showing that receivers are sensitive to barks and may use the occurrence of barks to make informed decisions regarding when it is favorable to join a hunt. Furthermore, we observed a decrease in the delay between the first sign of predatory interest and hunt initiation in events where barks occurred. When barks were produced, individuals had more information available regarding how many others were likely to hunt. When barks did not occur, party members may have required more time to obtain the same information, through direct observation of conspecific movements. Hence, while barks are not necessary for individuals to make decisions about joining a hunt (as they are only produced in ~40% of hunting events), when they are present, they may represent a valuable source of information to expedite such decisions.
Barks provide a potentially valuable source of information for receivers, and our data show that listeners are sensitive to the occurrence of barks. However, it is unclear which cognitive mechanisms are involved in driving receiver behavior in relation to barks. It is possible that simple mechanisms are involved, with barks potentially increasing arousal or excitement in receivers, making subsequent action and participation more likely. Alternatively, as outlined above, receivers may use barks to make informed decisions about the likelihood of success as well as costs and benefits of joining the hunt. This seems feasible given that artificial cooperation paradigms in captivity have shown chimpanzees to be sensitive to the likelihood Models provide compelling evidence that, compared to hunts where no barks were given before hunt initiation, in hunts where barks were produced before hunt initiation, (A) a larger proportion of animals in the party was recruited (binomial GLMM); (B) the proportional hazard of hunt initiation increased, i.e., the latency between the first sign of predatory interest and the onset of a hunt was shorter (Cox proportional hazards LM); and (D) the proportional hazard of a kill increased, i.e., the latency between the onset of a hunt and the time of the first kill was shorter (Cox proportional hazards LM). In contrast, regarding the probability of achieving a kill (C), we found no strong association between bark production and kill likelihood.
of success with partners of varying skill and tolerance (42)(43)(44). Whether driven by arousal or information, receiver sensitivity to barks shows social influence over hunting behavior, rendering the actions of hunters nonindependent (1). In addition, if increased participation following bark production arises from a perceived increment in the chances of success, then this would presuppose a knowledge of the causal role of hunting partners in achieving a kill, which constitutes a criterion for attributing an even further degree of coordination complexity (1). Future research should probe the cognitive mechanisms that mediate the relationship between calls and hunt participation in receivers, and this may help us better understand the extent of coordination involved in chimpanzee hunts.
Our data also raise questions concerning the cognitive mechanisms underlying bark production. First, the signal could be intentionally produced to communicate readiness to hunt (45). As the vocalization is reliably used in a cooperative context and has the potential to influence the decision-making of others, it could represent a first step toward the more intentional forms of communication-enhanced cooperation typical of our species. However, rigorous testing of caller behavior against established markers of intentionality (45) is required before we can evaluate this possibility. As mentioned above, a more parsimonious mechanism underlying bark production is excitement or arousal (46). Although our data cannot rigorously disentangle emotional versus more intentional mechanisms, which are not mutually exclusive, this represents a promising avenue for future research that will help shed further light on the similarities between human and great ape vocal coordination of cooperative behavior.
The mechanisms underlying the positive effect of barks on hunting success also remain speculative. Barks did not affect kill probability, which increased with the number of hunters and decreased with the presence of swollen females. However, bark occurrence was associated with a reduced latency in making a kill. We show that this effect was not a by-product of a larger number of hunters, but, rather, it was independently driven by the occurrence of barks. This suggests that the use of communication expedited the achievement of the desired outcome, supporting previous findings in distantly related species (7,47). The mechanisms driving the association between barking and faster kills are unknown but could include (i) the barks facilitating faster decisions in party members to join the hunt and (ii) the barks startling monkey prey and driving them toward their escape routes, making them easier to catch. Acoustically, chimpanzee barks are loud, sharp calls with sudden onsets (48), which may have distressed and repelled the monkeys. Future work probing the timing of monkey alarm calls and panic fleeing in relation to the timing of chimpanzee barks would test this possibility.
More generally, these findings have the potential to shed light on the evolutionary roots of the notable human capacity to vocally coordinate cooperative behavior. For example, in modern Ache huntergatherer societies in Paraguay, cooperation mediated by verbal communication is critical to successful hunting of nine-banded armadillos (Dasypus novemcinctus) and Cebus monkeys (family Cebidae) (49). Empirical work demonstrates that increased verbal activity in humans enhances coordination efforts (50). Our findings suggest that the behavioral dynamics we observed in hunting chimpanzees could represent precursors to a more typically hominin interplay of communication and cooperation. Within the primate order, vocally mediated polyadic cooperation is unlikely to be a de novo-evolved trait unique to humans but, instead, potentially has its origins rooted more deeply in the lineage and was likely present in at least the last common ancestor of humans and chimpanzees. In wild chimpanzees, barks not only possibly facilitate decisions regarding whether and how quickly to join a hunt but also influence the outcome of the group behavior: Hunting is more effective following a bark.

Study site and subjects
The Kanyawara chimpanzee community is located in Kibale National Park, Uganda. The community inhabits an area of at least ~15 km 2 within the northwestern region of the park, with at an altitude of ~1500 m, within a habitat comprising mainly evergreen forest and swathes of swamp. The population has been the object of long-term study since 1987 (51), and for the duration of this study, between 1996 and 2018, the community size ranged between 40 and 57 individuals.

Data collection and extraction
Since 1996, data on hunting behavior have been collected by trained Ugandan field assistants who record handwritten notes while following chimpanzees, documenting group-level information such as party size and composition and the proximity of any monkey species, as well as individual-level data concerning identity, behavior, sexual swellings, etc. At Kanyawara, a party is defined as all individuals within 50 m using a chain rule, and this variable is recorded every 15 min. All occurrences of hunting attempts (successful and unsuccessful) were recorded by the observing field assistants. Predatory interest in monkeys was defined as any overt gaze orientation or movement directed toward monkeys, while the initiation of a hunting event was defined as a chimpanzee climbing up to the height of the lowest monkey (28,33). While sometimes it may be difficult to pinpoint the exact start of a hunt, this definition is consistent with that used at other field sites such as Taï, where a hunter is any individual that "climbs up to the height in the canopy where the prey live, looking and orienting itself toward them" (33), and Gombe, where a hunter is one that is seen "climbing in pursuit of one or more monkeys" (36). During hunting, multiple field assistants coordinated to observe the behavior of as many individuals as possible. Behavioral changes were noted chronologically (hh:mm), providing details of actions performed, recipients involved, and outcomes. Immediately after the hunt, the information from all field assistants and other observers was collated into a predation-specific datasheet.
Data for subsequent analyses were extracted from the complementary information contained in the handwritten individual accounts and predation-specific datasheets. The full list of variables extracted from these sources is detailed in table S1. These data include variables reported directly on the datasheets by the field assistants, such as the full party size, identity of individuals, hunting party size, timing of when barks started to be produced and by whom, and hunt initiation. Additional variables, such as the delay in hunt initiation and the delay in obtaining a kill, were extracted from the detailed behavioral descriptions.

Statistical analyses
Our sample sizes for the five different statistical models varied slightly. This variation results from the availability of response variables for extraction. In the case of individual participation following a bark, this response variable was unambiguously recorded in 227 events, resulting in a sample size of 227. In the case of hunt participation, the response variable was unambiguously coded in 232 events. In the case of hunting delay, this specific response variable was unambiguously recorded in 230 events. In the case of kill probability, the response variable was unambiguously coded in 232 events. Last, in the case of delay to obtain a kill, this specific response variable was unambiguously recorded in 157 events. The sample size thus reflects the specific response variables of the different models.
We conducted analyses within a Bayesian framework using the "brms" and "rstan" packages (52,53) in R 3.6.3 (54). Given the naturalistic nature of the data, resulting in sometimes small and unbalanced sample sizes, we only considered up to second-order interaction effects in our first analysis (removing interactions for which model comparisons indicated that they did not enhance posterior predictive accuracy), and only the main effects in the four models composing our second analysis. Pseudo-replication within our first dataset, due to multiple observations on the same individual across multiple hunting events and multiple individuals participating in each event, was accounted for by including both individual ID and hunt ID as partially crossed random effects. An observation-level random effect was included in the first model of our second analysis (modeling the proportion of individuals recruited in each hunt) to absorb the overdispersion in this particular dataset (55).
Models were fitted by running four independent Markov chains (based on the Hamiltonian Monte Carlo algorithm with No-U-Turn Sampler) for 2000 warm-up and 2000 post-warm-up iterations each. We specified weakly regularizing priors to restrict the sampled parameter space and defined normal distributions for fixed intercepts ( = 0,  = 10) and predictor variables ( = 0,  = 5) while setting Cauchy distributions for estimates of the variance components representing random intercepts (x 0 = 0,  = 2). Chain mixing, stationarity, and convergence were confirmed by visual inspection of trace plots and by insisting on scale reduction factor values of 1 (R-hat = 1.00). To achieve this for all estimates across all models, the resolution of the sampler was increased by adjusting the "adapt_delta" parameter in the "brm()" function in R to 0.99. Ensuing concerns about algorithm efficiency were addressed by allowing a maximal tree depth of 15. Effective sample size for all parameter estimates in all models was >2000.
To assess model performance, we conducted graphical posterior predictive checks and calculated a Bayesian version of the R 2 statistic (56) whenever possible [note that neither procedure is implemented (yet) for Cox proportional hazards models (52)]. Candidate models were compared on the basis of their relative out-of-sample predictive accuracy using approximate leave-one-out cross-validation (57). This procedure calculates differences in the expected log pointwise probability density ( elpd ) of candidate models, in which models with larger values are deemed more plausible than models with lower values, given the data at hand. The full results from all models are shown in tables S3 and S4.
All event-level analyses were also run on the full dataset including events associated with barker uncertainty (n = 307) to rule out the possibility that results obtained were driven by the exclusion of certain types of events, e.g., larger hunting parties, where identifying more barkers is more challenging, rather than by the explanatory variables in our models. The pattern of results was consistent with our original set of analyses (see the Supplementary Materials for further details), suggesting that our findings were not driven by the exclusion of events with unidentified barkers.

SUPPLEMENTARY MATERIALS
Supplementary material for this article is available at https://science.org/doi/10.1126/ sciadv.abo5553 View/request a protocol for this paper from Bio-protocol.